Data Loading and Preparation

library(tidyverse)
library(lubridate)
library(ggplot2)
library(plotly)
library(DT)
library(kableExtra)
library(viridis)
library(gridExtra)

df <- read.csv("Collection Observations - Birds Beak-2.csv", stringsAsFactors = FALSE)

# Display the structure of the data
str(df)

## 'data.frame':    86 obs. of  15 variables:
##  $ Date            : chr  "6/19/2025" "6/19/2025" "6/19/2025" "6/19/2025" ...
##  $ Week            : chr  "1b" "1b" "1b" "1b" ...
##  $ Site..          : int  1 2 3 4 3 2 4 1 2 1 ...
##  $ Camera..        : int  1 2 1 2 1 2 1 2 1 2 ...
##  $ Start.Time      : chr  "11:00:00 AM" "11:00:00 AM" "11:30:00 AM" "11:30:00 AM" ...
##  $ End.Time        : chr  "11:30:00 AM" "11:30:00 AM" "12:00:00 PM" "12:00:00 PM" ...
##  $ Weather         : int  66 66 68 68 68 68 68 68 69 69 ...
##  $ Conditions      : chr  "overcast" "overcast" "overcast" "overcast" ...
##  $ Wind            : num  2.5 2.5 5.5 5.5 4.8 4.8 4.5 4.5 6.1 6.1 ...
##  $ Wind.direction  : chr  "S " "S " "SW" "SW" ...
##  $ Activity.on.site: chr  "none" "none" "none" "none" ...
##  $ Activity.around : chr  "none" "none" "none" "small bird flying over plant, landing on stakes" ...
##  $ Video.Duration  : chr  "30 min" "30 min" "30 min" "9 min" ...
##  $ Shift.type      : chr  "Morning" "Morning" "Morning" "Morning" ...
##  $ Notes           : chr  "" "" "" "?" ...

head(df)

##        Date Week Site.. Camera..  Start.Time    End.Time Weather Conditions
## 1 6/19/2025   1b      1        1 11:00:00 AM 11:30:00 AM      66   overcast
## 2 6/19/2025   1b      2        2 11:00:00 AM 11:30:00 AM      66   overcast
## 3 6/19/2025   1b      3        1 11:30:00 AM 12:00:00 PM      68   overcast
## 4 6/19/2025   1b      4        2 11:30:00 AM 12:00:00 PM      68   overcast
## 5 6/19/2025   1b      3        1 12:00:00 PM 12:30:00 PM      68   overcast
## 6 6/19/2025   1b      2        2 12:00:00 PM 12:30:00 PM      68   overcast
##   Wind Wind.direction Activity.on.site
## 1  2.5             S              none
## 2  2.5             S              none
## 3  5.5             SW             none
## 4  5.5             SW             none
## 5  4.8             SW             none
## 6  4.8             SW             none
##                                   Activity.around Video.Duration Shift.type
## 1                                            none         30 min    Morning
## 2                                            none         30 min    Morning
## 3                                            none         30 min    Morning
## 4 small bird flying over plant, landing on stakes          9 min    Morning
## 5 small bird flying over plant, landing on stakes         30 min        Mid
## 6                                            none      13.07 min        Mid
##   Notes
## 1      
## 2      
## 3      
## 4     ?
## 5      
## 6     ?

Data Cleaning and Processing

# Clean and process the data
df_clean <- df %>%
  # Remove empty rows
  filter(!is.na(Date) & Date != "") %>%
  
  # Convert date and time columns
  mutate(
    Date = mdy(Date),
    Start.Time = hms(Start.Time),
    End.Time = hms(End.Time),
    
    # Extract numeric values from wind speed
    Wind = as.numeric(Wind),
    
    # Clean temperature (assuming it's in Fahrenheit)
    Temperature = as.numeric(Weather),
    
    # Create a datetime column for start time
    DateTime = ymd_hms(paste(Date, Start.Time)),
    
    # Extract bee counts from Activity columns
    Bombus_on_site = case_when(
      str_detect(tolower(Activity.on.site), "bombus") ~ str_extract_all(Activity.on.site, "\\d+") %>% 
        map_chr(~ifelse(length(.x) > 0, .x[1], "1")),
      str_detect(tolower(Activity.on.site), "none|na") ~ "0",
      TRUE ~ "0"
    ),
    
    Bombus_around = case_when(
      str_detect(tolower(Activity.around), "bombus") ~ str_extract_all(Activity.around, "\\d+") %>% 
        map_chr(~ifelse(length(.x) > 0, .x[1], "1")),
      str_detect(tolower(Activity.around), "none|na") ~ "0",
      TRUE ~ "0"
    ),
    
    # Convert to numeric
    Bombus_on_site = as.numeric(Bombus_on_site),
    Bombus_around = as.numeric(Bombus_around),
    
    # Total bombus count
    Total_Bombus = Bombus_on_site + Bombus_around,
    
    # Create week number
    Week_num = as.numeric(str_extract(Week, "\\d+")),
    
    # Convert video duration to minutes
    Video_Duration_min = case_when(
      str_detect(Video.Duration, "min") ~ as.numeric(str_extract(Video.Duration, "\\d+\\.?\\d*")),
      TRUE ~ 30  # Default to 30 minutes
    )
  ) %>%
  
  # Filter out rows with missing essential data
  filter(!is.na(Date), !is.na(Site..))

# Display cleaned data summary
summary(df_clean)

##       Date                Week               Site..        Camera..  
##  Min.   :2025-06-19   Length:82          Min.   :1.00   Min.   :1.0  
##  1st Qu.:2025-06-23   Class :character   1st Qu.:1.25   1st Qu.:1.0  
##  Median :2025-06-26   Mode  :character   Median :2.50   Median :1.5  
##  Mean   :2025-06-27                      Mean   :2.50   Mean   :1.5  
##  3rd Qu.:2025-07-03                      3rd Qu.:3.75   3rd Qu.:2.0  
##  Max.   :2025-07-09                      Max.   :4.00   Max.   :2.0  
##                                                                      
##    Start.Time            End.Time                           Weather     
##  Min.   :1H 0M 0S     Min.   :1H 0M 0S                   Min.   :61.00  
##  1st Qu.:3H 0M 0S     1st Qu.:3H 0M 0S                   1st Qu.:65.00  
##  Median :5H 55M 0S    Median :4H 30M 0S                  Median :68.00  
##  Mean   :6H 38M 45S   Mean   :6H 24M 24.4444444444453S   Mean   :67.32  
##  3rd Qu.:10H 7M 30S   3rd Qu.:10H 30M 0S                 3rd Qu.:69.00  
##  Max.   :12H 30M 0S   Max.   :12H 30M 0S                 Max.   :73.00  
##  NA's   :2            NA's   :1                                         
##   Conditions             Wind       Wind.direction     Activity.on.site  
##  Length:82          Min.   :2.100   Length:82          Length:82         
##  Class :character   1st Qu.:2.900   Class :character   Class :character  
##  Mode  :character   Median :4.500   Mode  :character   Mode  :character  
##                     Mean   :4.698                                        
##                     3rd Qu.:6.600                                        
##                     Max.   :8.400                                        
##                                                                          
##  Activity.around    Video.Duration      Shift.type           Notes          
##  Length:82          Length:82          Length:82          Length:82         
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##                                                                             
##   Temperature       DateTime                   Bombus_on_site   
##  Min.   :61.00   Min.   :2025-06-19 03:00:00   Min.   :0.00000  
##  1st Qu.:65.00   1st Qu.:2025-06-23 05:00:00   1st Qu.:0.00000  
##  Median :68.00   Median :2025-06-25 08:15:00   Median :0.00000  
##  Mean   :67.32   Mean   :2025-06-27 20:26:45   Mean   :0.09756  
##  3rd Qu.:69.00   3rd Qu.:2025-07-03 09:37:30   3rd Qu.:0.00000  
##  Max.   :73.00   Max.   :2025-07-09 11:30:00   Max.   :2.00000  
##                  NA's   :2                                      
##  Bombus_around     Total_Bombus       Week_num     Video_Duration_min
##  Min.   :0.0000   Min.   :0.0000   Min.   :1.000   Min.   : 2.00     
##  1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:2.000   1st Qu.:30.00     
##  Median :0.0000   Median :0.0000   Median :2.000   Median :30.00     
##  Mean   :0.1341   Mean   :0.2317   Mean   :2.439   Mean   :27.56     
##  3rd Qu.:0.0000   3rd Qu.:0.0000   3rd Qu.:3.000   3rd Qu.:30.00     
##  Max.   :2.0000   Max.   :3.0000   Max.   :4.000   Max.   :30.00     
##

Exploratory Data Analysis

Data Overview

# Basic statistics table
overview_stats <- df_clean %>%
  summarise(
    Total_Observations = n(),
    Unique_Sites = n_distinct(Site..),
    Date_Range = paste(min(Date, na.rm = TRUE), "to", max(Date, na.rm = TRUE)),
    Total_Bombus_Observed = sum(Total_Bombus, na.rm = TRUE),
    Avg_Bombus_per_Session = round(mean(Total_Bombus, na.rm = TRUE), 2),
    Sessions_with_Bombus = sum(Total_Bombus > 0, na.rm = TRUE),
    Percent_Sessions_with_Bombus = round((sum(Total_Bombus > 0, na.rm = TRUE) / n()) * 100, 1)
  )

kable(overview_stats, caption = "Dataset Overview") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Dataset Overview
Total_Observations	Unique_Sites	Date_Range	Total_Bombus_Observed	Avg_Bombus_per_Session	Sessions_with_Bombus	Percent_Sessions_with_Bombus
82	4	2025-06-19 to 2025-07-09	19	0.23	11	13.4

Bombus Activity Over Time

# Daily bombus activity
daily_activity <- df_clean %>%
  group_by(Date) %>%
  summarise(
    Total_Bombus = sum(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    Avg_Bombus = round(Total_Bombus / Sessions, 2),
    .groups = 'drop'
  )

# Time series plot
p1 <- ggplot(daily_activity, aes(x = Date, y = Total_Bombus)) +
  geom_line(color = "steelblue", size = 1) +
  geom_point(color = "darkblue", size = 2) +
  labs(title = "Daily Bombus Activity",
       x = "Date",
       y = "Total Bombus Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Average bombus per session
p2 <- ggplot(daily_activity, aes(x = Date, y = Avg_Bombus)) +
  geom_line(color = "orange", size = 1) +
  geom_point(color = "darkorange", size = 2) +
  labs(title = "Average Bombus per Session",
       x = "Date",
       y = "Average Bombus Count") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

grid.arrange(p1, p2, ncol = 1)

Bombus Activity by Site

# Bombus activity by site
site_activity <- df_clean %>%
  group_by(Site..) %>%
  summarise(
    Total_Bombus = sum(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    Avg_Bombus = round(Total_Bombus / Sessions, 2),
    Sessions_with_Bombus = sum(Total_Bombus > 0, na.rm = TRUE),
    Percent_with_Bombus = round((Sessions_with_Bombus / Sessions) * 100, 1),
    .groups = 'drop'
  )

# Bar plot of total bombus by site
p3 <- ggplot(site_activity, aes(x = factor(Site..), y = Total_Bombus, fill = factor(Site..))) +
  geom_bar(stat = "identity", alpha = 0.7) +
  scale_fill_viridis_d() +
  labs(title = "Total Bombus Count by Site",
       x = "Site Number",
       y = "Total Bombus Count") +
  theme_minimal() +
  theme(legend.position = "none")

# Percentage of sessions with bombus by site
p4 <- ggplot(site_activity, aes(x = factor(Site..), y = Percent_with_Bombus, fill = factor(Site..))) +
  geom_bar(stat = "identity", alpha = 0.7) +
  scale_fill_viridis_d() +
  labs(title = "Percentage of Sessions with Bombus by Site",
       x = "Site Number",
       y = "Percentage of Sessions") +
  theme_minimal() +
  theme(legend.position = "none")

grid.arrange(p3, p4, ncol = 1)

# Display site statistics table
kable(site_activity, caption = "Bombus Activity by Site") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Bombus Activity by Site
Site..	Total_Bombus	Sessions	Avg_Bombus	Sessions_with_Bombus	Percent_with_Bombus
1	4	21	0.19	1	4.8
2	6	20	0.30	1	5.0
3	4	20	0.20	1	5.0
4	5	21	0.24	1	4.8

Environmental Factors and Bombus Activity

# Temperature vs Bombus activity
temp_analysis <- df_clean %>%
  filter(!is.na(Temperature)) %>%
  group_by(Temperature) %>%
  summarise(
    Avg_Bombus = mean(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    .groups = 'drop'
  )

p5 <- ggplot(df_clean, aes(x = Temperature, y = Total_Bombus)) +
  geom_point(alpha = 0.6, color = "darkgreen") +
  geom_smooth(method = "lm", se = TRUE, color = "red") +
  labs(title = "Temperature vs Bombus Activity",
       x = "Temperature (°F)",
       y = "Total Bombus Count") +
  theme_minimal()

# Wind speed vs Bombus activity
p6 <- ggplot(df_clean, aes(x = Wind, y = Total_Bombus)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  geom_smooth(method = "lm", se = TRUE, color = "red") +
  labs(title = "Wind Speed vs Bombus Activity",
       x = "Wind Speed",
       y = "Total Bombus Count") +
  theme_minimal()

grid.arrange(p5, p6, ncol = 1)

Time of Day Analysis

# Convert start time to hours for analysis
df_clean <- df_clean %>%
  mutate(
    Start_Hour = hour(Start.Time),
    Time_Period = case_when(
      Start_Hour < 12 ~ "Morning",
      Start_Hour < 15 ~ "Midday",
      TRUE ~ "Afternoon"
    )
  )

# Bombus activity by time period
time_activity <- df_clean %>%
  group_by(Time_Period) %>%
  summarise(
    Total_Bombus = sum(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    Avg_Bombus = round(Total_Bombus / Sessions, 2),
    .groups = 'drop'
  )

p7 <- ggplot(time_activity, aes(x = Time_Period, y = Avg_Bombus, fill = Time_Period)) +
  geom_bar(stat = "identity", alpha = 0.7) +
  scale_fill_viridis_d() +
  labs(title = "Average Bombus Activity by Time Period",
       x = "Time Period",
       y = "Average Bombus Count") +
  theme_minimal() +
  theme(legend.position = "none")

# Hour-by-hour activity
hourly_activity <- df_clean %>%
  group_by(Start_Hour) %>%
  summarise(
    Total_Bombus = sum(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    Avg_Bombus = round(Total_Bombus / Sessions, 2),
    .groups = 'drop'
  )

p8 <- ggplot(hourly_activity, aes(x = Start_Hour, y = Avg_Bombus)) +
  geom_line(color = "purple", size = 1) +
  geom_point(color = "blue", size = 2) +
  labs(title = "Bombus Activity Throughout the Day",
       x = "Hour of Day",
       y = "Average Bombus Count") +
  theme_minimal() +
  scale_x_continuous(breaks = seq(9, 18, 1))

grid.arrange(p7, p8, ncol = 1)

Weather Conditions Analysis

# Clean weather conditions
df_clean <- df_clean %>%
  mutate(
    Weather_Clean = case_when(
      str_detect(tolower(Conditions), "sunny") ~ "Sunny",
      str_detect(tolower(Conditions), "overcast") ~ "Overcast",
      str_detect(tolower(Conditions), "cloudy") ~ "Cloudy",
      TRUE ~ "Other"
    )
  )

# Bombus activity by weather condition
weather_activity <- df_clean %>%
  group_by(Weather_Clean) %>%
  summarise(
    Total_Bombus = sum(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    Avg_Bombus = round(Total_Bombus / Sessions, 2),
    Sessions_with_Bombus = sum(Total_Bombus > 0, na.rm = TRUE),
    Percent_with_Bombus = round((Sessions_with_Bombus / Sessions) * 100, 1),
    .groups = 'drop'
  )

p9 <- ggplot(weather_activity, aes(x = Weather_Clean, y = Avg_Bombus, fill = Weather_Clean)) +
  geom_bar(stat = "identity", alpha = 0.7) +
  scale_fill_viridis_d() +
  labs(title = "Average Bombus Activity by Weather Condition",
       x = "Weather Condition",
       y = "Average Bombus Count") +
  theme_minimal() +
  theme(legend.position = "none")

print(p9)

# Display weather statistics table
kable(weather_activity, caption = "Bombus Activity by Weather Condition") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Bombus Activity by Weather Condition
Weather_Clean	Total_Bombus	Sessions	Avg_Bombus	Sessions_with_Bombus	Percent_with_Bombus
Cloudy	0	10	0.00	0	0.0
Overcast	4	26	0.15	1	3.8
Sunny	15	46	0.33	1	2.2

Weekly Progress Analysis

# Weekly bombus activity
weekly_activity <- df_clean %>%
  group_by(Week_num) %>%
  summarise(
    Total_Bombus = sum(Total_Bombus, na.rm = TRUE),
    Sessions = n(),
    Avg_Bombus = round(Total_Bombus / Sessions, 2),
    Sessions_with_Bombus = sum(Total_Bombus > 0, na.rm = TRUE),
    Percent_with_Bombus = round((Sessions_with_Bombus / Sessions) * 100, 1),
    .groups = 'drop'
  )

p10 <- ggplot(weekly_activity, aes(x = Week_num, y = Total_Bombus)) +
  geom_line(color = "darkgreen", size = 1) +
  geom_point(color = "darkgreen", size = 3) +
  labs(title = "Weekly Bombus Activity Trend",
       x = "Week Number",
       y = "Total Bombus Count") +
  theme_minimal() +
  scale_x_continuous(breaks = unique(weekly_activity$Week_num))

print(p10)

# Display weekly statistics table
kable(weekly_activity, caption = "Weekly Bombus Activity Summary") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Weekly Bombus Activity Summary
Week_num	Total_Bombus	Sessions	Avg_Bombus	Sessions_with_Bombus	Percent_with_Bombus
1	7	16	0.44	1	6.2
2	0	34	0.00	0	0.0
3	8	12	0.67	1	8.3
4	4	20	0.20	1	5.0

Data Quality Assessment

# Check for missing data and technical issues
quality_check <- df_clean %>%
  mutate(
    Has_Technical_Issue = str_detect(tolower(Notes), "died|overheated|memory|card|froze"),
    Full_Duration = Video_Duration_min >= 29  # Sessions that recorded close to full 30 minutes
  ) %>%
  summarise(
    Total_Sessions = n(),
    Sessions_with_Issues = sum(Has_Technical_Issue, na.rm = TRUE),
    Percent_with_Issues = round((Sessions_with_Issues / Total_Sessions) * 100, 1),
    Full_Duration_Sessions = sum(Full_Duration, na.rm = TRUE),
    Percent_Full_Duration = round((Full_Duration_Sessions / Total_Sessions) * 100, 1),
    Missing_Bombus_Data = sum(is.na(Total_Bombus)),
    .groups = 'drop'
  )

kable(quality_check, caption = "Data Quality Assessment") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))

Data Quality Assessment
Total_Sessions	Sessions_with_Issues	Percent_with_Issues	Full_Duration_Sessions	Percent_Full_Duration	Missing_Bombus_Data
82	18	22	68	82.9	0

# Technical issues by date
technical_issues <- df_clean %>%
  mutate(Has_Technical_Issue = str_detect(tolower(Notes), "died|overheated|memory|card|froze")) %>%
  group_by(Date) %>%
  summarise(
    Total_Sessions = n(),
    Sessions_with_Issues = sum(Has_Technical_Issue, na.rm = TRUE),
    Percent_with_Issues = round((Sessions_with_Issues / Total_Sessions) * 100, 1),
    .groups = 'drop'
  ) %>%
  filter(Sessions_with_Issues > 0)

if(nrow(technical_issues) > 0) {
  kable(technical_issues, caption = "Technical Issues by Date") %>%
    kable_styling(bootstrap_options = c("striped", "hover"))
}

Technical Issues by Date
Date	Total_Sessions	Sessions_with_Issues	Percent_with_Issues
2025-06-19	12	4	33.3
2025-06-20	4	4	100.0
2025-06-23	12	4	33.3
2025-06-26	10	2	20.0
2025-07-03	12	2	16.7
2025-07-07	12	2	16.7

Interactive Data Exploration

# Create interactive plot for bombus activity over time
interactive_plot <- df_clean %>%
  plot_ly(x = ~DateTime, y = ~Total_Bombus, 
          color = ~factor(Site..), 
          size = ~Video_Duration_min,
          text = ~paste("Site:", Site.., "<br>",
                       "Date:", Date, "<br>",
                       "Time:", Start.Time, "<br>",
                       "Bombus Count:", Total_Bombus, "<br>",
                       "Weather:", Weather_Clean, "<br>",
                       "Temperature:", Temperature, "°F", "<br>",
                       "Wind Speed:", Wind),
          hovertemplate = "%{text}<extra></extra>") %>%
  add_markers() %>%
  layout(title = "Interactive Bombus Activity Timeline",
         xaxis = list(title = "Date and Time"),
         yaxis = list(title = "Total Bombus Count"),
         showlegend = TRUE)

interactive_plot

Summary and Recommendations

Key Findings

# Calculate key metrics for summary
summary_metrics <- df_clean %>%
  summarise(
    Peak_Activity_Date = Date[which.max(Total_Bombus)],
    Peak_Activity_Count = max(Total_Bombus, na.rm = TRUE),
    Most_Active_Site = Site..[which.max(Total_Bombus)],
    Best_Weather = weather_activity$Weather_Clean[which.max(weather_activity$Avg_Bombus)],
    Optimal_Temperature = round(mean(df_clean$Temperature[df_clean$Total_Bombus > 0], na.rm = TRUE), 1),
    Best_Time_Period = time_activity$Time_Period[which.max(time_activity$Avg_Bombus)],
    .groups = 'drop'
  )

cat("## Key Findings:\n\n")

## ## Key Findings:

cat("- **Peak Activity Date:** ", as.character(summary_metrics$Peak_Activity_Date), 
    " (", summary_metrics$Peak_Activity_Count, " bombus observed)\n")

## - **Peak Activity Date:**  2025-07-03  ( 3  bombus observed)

cat("- **Most Active Site:** Site ", summary_metrics$Most_Active_Site, "\n")

## - **Most Active Site:** Site  4

cat("- **Best Weather Conditions:** ", summary_metrics$Best_Weather, "\n")

## - **Best Weather Conditions:**  Sunny

cat("- **Optimal Temperature:** ", summary_metrics$Optimal_Temperature, "°F\n")

## - **Optimal Temperature:**  69.9 °F

cat("- **Best Time Period:** ", summary_metrics$Best_Time_Period, "\n")

## - **Best Time Period:**  Morning

cat("- **Overall Success Rate:** ", overview_stats$Percent_Sessions_with_Bombus, "% of sessions had bombus activity\n")

## - **Overall Success Rate:**  13.4 % of sessions had bombus activity

Recommendations for Future Data Collection

Based on the analysis, here are recommendations for your future data collection and AI model training:

Optimal Collection Conditions:
- Focus on sunny weather conditions when possible
- Target the most productive time periods identified in the analysis
- Consider environmental factors like temperature and wind speed
Site Strategy:
- Prioritize sites with higher bombus activity rates
- Consider expanding monitoring at successful sites
Technical Improvements:
- Address camera reliability issues (overheating, memory cards)
- Ensure consistent 30-minute recording sessions
- Consider backup equipment for high-activity periods
Data for AI Training:
- Current dataset provides good baseline for training data
- Include environmental metadata with video annotations
- Consider balancing dataset with negative examples (no bombus activity)
Future Analysis:
- Track seasonal patterns as data accumulates
- Correlate with plant phenology data if available
- Consider pollen load analysis for bombus specimens

Analysis completed on 2025-07-09

Bird’s Beak Plant Bombus Bee Observation Analysis

Your Name

2025-07-09

Data Loading and Preparation

Data Cleaning and Processing

Exploratory Data Analysis

Data Overview

Bombus Activity Over Time

Bombus Activity by Site

Environmental Factors and Bombus Activity

Time of Day Analysis

Weather Conditions Analysis

Weekly Progress Analysis

Data Quality Assessment

Interactive Data Exploration

Summary and Recommendations

Key Findings

Recommendations for Future Data Collection